Machine Learning
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.

Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸ“Œ Glitches in the Attention Matrix

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2026-01-14 | ⏱️ Read time: 13 min read

A history of Transformer artifacts and the latest research on how to fix them

#DataScience #AI #Python
πŸ“Œ Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-01-14 | ⏱️ Read time: 15 min read

Seeded topic modeling, integration with LLMs, and training on summarized data are the fresh parts…

#DataScience #AI #Python
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πŸ“Œ When Shapley Values Break: A Guide to Robust Model Explainability

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2026-01-15 | ⏱️ Read time: 9 min read

Shapley Values are one of the most common methods for explainability, yet they can be…

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πŸ“Œ How to Run Coding Agents in Parallel

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-01-15 | ⏱️ Read time: 8 min read

Get the most out of Claude Code

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πŸ“Œ The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon

πŸ—‚ Category: PRODUCTIVITY

πŸ•’ Date: 2026-01-15 | ⏱️ Read time: 8 min read

Designing a centralized system to track daily habits and long-term goals

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πŸ“Œ Do You Smell That? Hidden Technical Debt in AI Development

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2026-01-15 | ⏱️ Read time: 14 min read

Why speed without standards creates fragile AI products

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πŸ“Œ Maximum-Effiency Coding Setup

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2026-01-16 | ⏱️ Read time: 9 min read

Learn how to be a more efficient programmer

#DataScience #AI #Python
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YOLO Training Template

Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created.

The platform allows:

β€’ to upload your own dataset
β€’ to label manually or auto-label via DINOv3
β€’ to enrich the data if desired
β€’ to train a #YOLO model on your own data
β€’ to run inference immediately
β€’ to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones

All of this is available for free and can already be tested on #GitHub.

Repo:
https://github.com/computer-vision-with-marco/yolo-training-template

https://t.iss.one/CodeProgrammer
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πŸ“Œ Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2026-01-16 | ⏱️ Read time: 18 min read

Why your final LLM layer is OOMing and how to fix it with a custom…

#DataScience #AI #Python
πŸ“Œ From RGB to Lab: Addressing Color Artifacts in AI Image Compositing

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2026-01-16 | ⏱️ Read time: 13 min read

A multi-tier approach to segmentation, color correction, and domain-specific enhancement

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πŸ“Œ The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-16 | ⏱️ Read time: 13 min read

Acquisitions, venture, and an increasingly competitive landscape all point to a market ceiling

#DataScience #AI #Python